Super Resolution using CNN¶
Important: Before proceed, go to Edit -> Notebook settings to make sure you've selected TPU as Hardware accelerator
Preparation¶
Download data from GCS¶
In [1]:
!gsutil cp gs://project3ads/image_data.tar.gz dataset.tar.gz
!gunzip -c dataset.tar.gz | tar xopf -
Copying gs://project3ads/image_data.tar.gz... \ [1 files][215.5 MiB/215.5 MiB] Operation completed over 1 objects/215.5 MiB.
In [0]:
path_HR = "./train_set/HR/"
path_LR = "./train_set/LR/"
In [0]:
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
def plot_img(img):
plt.imshow(img)
plt.axis('off')
plt.show()
Split training, testing and validation set¶
In [0]:
# split to training, test and validation set
import random
import math
import os
train_percentage = 0.7
validation_percentage = 0.2
test_percentage = 1 - train_percentage - validation_percentage
imgs_name = np.unique(np.array([x.replace('._','') for x in os.listdir(path_HR)]))
n = len(imgs_name)
indices = np.array(list(range(0, n)))
# randomly draw train_percentage * n samples from that array and move corresponding files to train
train_and_val = np.array(random.sample(range(0, n), math.floor((train_percentage+validation_percentage) * n)))
train_indices = train_and_val[0: math.floor(train_percentage*n)+1]
val_indices = train_and_val[math.floor(train_percentage*n)+1: ]
# randomly draw validation_percentage * n samples from the rest and move to valid
test_indices = np.delete(indices, train_and_val)
train_n = len(train_indices)
val_n = len(val_indices)
test_n = len(test_indices)
Define generators to feed data into the model for training purpose¶
In [0]:
target_shape = (430, 568, 3)
def preprocess_img(name, method):
input_img = Image.open(path_LR + name)
output_img = Image.open(path_HR + name)
if method == 'flip':
input_img = input_img.transpose(Image.FLIP_TOP_BOTTOM)
output_img = output_img.transpose(Image.FLIP_TOP_BOTTOM)
input_img = input_img.resize((target_shape[1], target_shape[0]), Image.BICUBIC)
output_img = output_img.resize((target_shape[1], target_shape[0]), Image.BICUBIC)
input_img = np.array(input_img)
output_img = np.array(output_img)
input_img = input_img / 255.0
output_img = output_img / 255.0
return (input_img, output_img)
def image_generator(input_path, output_path, names, batch_size = 16):
while True:
num_of_images = math.floor(batch_size / 2)
batch_paths = np.random.choice(a = names,
size = num_of_images)
batch_input = np.zeros([batch_size, target_shape[0], target_shape[1], 3])
batch_output = np.zeros([batch_size, target_shape[0], target_shape[1], 3])
i = 0
# Read in each input, perform preprocessing and get labels
for name in batch_paths:
(input_img, output_img) = preprocess_img(name, 'normal')
batch_input[i] = input_img
batch_output[i] = output_img
i += 1
if i >= batch_size:
break
(input_img, output_img) = preprocess_img(name, 'flip')
batch_input[i] = input_img
batch_output[i] = output_img
i += 1
if i >= batch_size:
break
# Return a tuple of (input,output) to feed the network
batch_x = np.array( batch_input )
batch_y = np.array( batch_output )
yield( batch_x, batch_y )
def val_image_generator(input_path, output_path, names, batch_size = 16):
while True:
batch_paths = np.random.choice(a = names,
size = batch_size)
batch_input = np.zeros([batch_size, target_shape[0], target_shape[1], 3])
batch_output = np.zeros([batch_size, target_shape[0], target_shape[1], 3])
i = 0
# Read in each input, perform preprocessing and get labels
for name in batch_paths:
(input_img, output_img) = preprocess_img(name, 'normal')
batch_input[i] = input_img
batch_output[i] = output_img
i += 1
# Return a tuple of (input,output) to feed the network
batch_x = np.array( batch_input )
batch_y = np.array( batch_output )
yield( batch_x, batch_y )
Model definition¶
Use the model mentioned here as a reference: DSRCNN model https://github.com/titu1994/Image-Super-Resolution
In [7]:
import tensorflow as tf
init = tf.keras.layers.Input(shape = target_shape)
level1_1 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(init)
level2_1 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(level1_1)
level2_2 = tf.keras.layers.Convolution2DTranspose(64, (3, 3), activation='relu', padding='same')(level2_1)
level2 = tf.keras.layers.Add()([level2_1, level2_2])
level1_2 = tf.keras.layers.Convolution2DTranspose(64, (3, 3), activation='relu', padding='same')(level2)
level1 = tf.keras.layers.Add()([level1_1, level1_2])
decoded = tf.keras.layers.Conv2D(3, (5, 5), activation='linear', padding='same')(level1)
model = tf.keras.models.Model(init, decoded)
model.summary()
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 430, 568, 3) 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 430, 568, 64) 1792 input_1[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 430, 568, 64) 36928 conv2d[0][0]
__________________________________________________________________________________________________
conv2d_transpose (Conv2DTranspo (None, 430, 568, 64) 36928 conv2d_1[0][0]
__________________________________________________________________________________________________
add (Add) (None, 430, 568, 64) 0 conv2d_1[0][0]
conv2d_transpose[0][0]
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 430, 568, 64) 36928 add[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 430, 568, 64) 0 conv2d[0][0]
conv2d_transpose_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 430, 568, 3) 4803 add_1[0][0]
==================================================================================================
Total params: 117,379
Trainable params: 117,379
Non-trainable params: 0
__________________________________________________________________________________________________
In [9]:
tpu_model = tf.contrib.tpu.keras_to_tpu_model(
model,
strategy=tf.contrib.tpu.TPUDistributionStrategy(
tf.contrib.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
)
)
def PSNRLoss(y_true, y_pred):
"""
PSNR is Peek Signal to Noise Ratio, which is similar to mean squared error.
It can be calculated as
PSNR = 20 * log10(MAXp) - 10 * log10(MSE)
"""
return -10. * tf.keras.backend.log(tf.keras.backend.mean(tf.keras.backend.square(y_pred - y_true))) / tf.keras.backend.log(10.)
tpu_model.compile(
optimizer=tf.train.AdamOptimizer(learning_rate=1e-3, ),
loss='mse',
metrics=[PSNRLoss]
)
INFO:tensorflow:Querying Tensorflow master (b'grpc://10.116.66.106:8470') for TPU system metadata. INFO:tensorflow:Found TPU system: INFO:tensorflow:*** Num TPU Cores: 8 INFO:tensorflow:*** Num TPU Workers: 1 INFO:tensorflow:*** Num TPU Cores Per Worker: 8 INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 12956587062940411332) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 3902986436435976879) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 18245817929401484669) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 3056968668840321071) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 17971001194593511051) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 1836448017145217420) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 14135634331263601330) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 10872499331674208666) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 11171400342654437960) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 5433600022953917723) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 8969544923885558965) INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 14126355292486778555) WARNING:tensorflow:tpu_model (from tensorflow.contrib.tpu.python.tpu.keras_support) is experimental and may change or be removed at any time, and without warning.
fit the model with data generator. Will take ~12mins for training¶
In [10]:
tpu_model.fit_generator(
image_generator(path_LR, path_HR, imgs_name[train_indices]),
epochs=10,
steps_per_epoch=50,
validation_data= val_image_generator(path_LR, path_HR, imgs_name[val_indices]),
validation_steps=10,
)
Epoch 1/10 INFO:tensorflow:New input shapes; (re-)compiling: mode=train (# of cores 8), [TensorSpec(shape=(2,), dtype=tf.int32, name='core_id0'), TensorSpec(shape=(2, 430, 568, 3), dtype=tf.float32, name='input_1_20'), TensorSpec(shape=(2, 430, 568, 3), dtype=tf.float32, name='conv2d_2_target_30')] INFO:tensorflow:Overriding default placeholder. INFO:tensorflow:Remapping placeholder for input_1 INFO:tensorflow:Started compiling INFO:tensorflow:Finished compiling. Time elapsed: 7.5499186515808105 secs INFO:tensorflow:Setting weights on TPU model. 49/50 [============================>.] - ETA: 1s - loss: 0.0163 - PSNRLoss: 20.8515INFO:tensorflow:New input shapes; (re-)compiling: mode=eval (# of cores 8), [TensorSpec(shape=(2,), dtype=tf.int32, name='core_id_10'), TensorSpec(shape=(2, 430, 568, 3), dtype=tf.float32, name='input_1_20'), TensorSpec(shape=(2, 430, 568, 3), dtype=tf.float32, name='conv2d_2_target_30')] INFO:tensorflow:Overriding default placeholder. INFO:tensorflow:Remapping placeholder for input_1 INFO:tensorflow:Started compiling INFO:tensorflow:Finished compiling. Time elapsed: 4.7651526927948 secs 50/50 [==============================] - 78s 2s/step - loss: 0.0160 - PSNRLoss: 20.9222 - val_loss: 0.0034 - val_PSNRLoss: 24.7687 Epoch 2/10 50/50 [==============================] - 61s 1s/step - loss: 0.0032 - PSNRLoss: 25.0811 - val_loss: 0.0024 - val_PSNRLoss: 26.3168 Epoch 3/10 50/50 [==============================] - 61s 1s/step - loss: 0.0026 - PSNRLoss: 25.9845 - val_loss: 0.0022 - val_PSNRLoss: 26.6618 Epoch 4/10 50/50 [==============================] - 61s 1s/step - loss: 0.0025 - PSNRLoss: 26.1448 - val_loss: 0.0019 - val_PSNRLoss: 27.1838 Epoch 5/10 50/50 [==============================] - 62s 1s/step - loss: 0.0023 - PSNRLoss: 26.5708 - val_loss: 0.0019 - val_PSNRLoss: 27.2108 Epoch 6/10 50/50 [==============================] - 61s 1s/step - loss: 0.0022 - PSNRLoss: 26.9052 - val_loss: 0.0019 - val_PSNRLoss: 27.1526 Epoch 7/10 50/50 [==============================] - 60s 1s/step - loss: 0.0021 - PSNRLoss: 26.9539 - val_loss: 0.0019 - val_PSNRLoss: 27.4276 Epoch 8/10 50/50 [==============================] - 61s 1s/step - loss: 0.0021 - PSNRLoss: 26.9051 - val_loss: 0.0018 - val_PSNRLoss: 27.4684 Epoch 9/10 50/50 [==============================] - 61s 1s/step - loss: 0.0020 - PSNRLoss: 27.0876 - val_loss: 0.0020 - val_PSNRLoss: 27.0368 Epoch 10/10 50/50 [==============================] - 61s 1s/step - loss: 0.0021 - PSNRLoss: 26.8913 - val_loss: 0.0019 - val_PSNRLoss: 27.2002
Out[10]:
<tensorflow.python.keras.callbacks.History at 0x7faee2ca7160>
Save trained model
In [12]:
tpu_model.save('tpu_model')
INFO:tensorflow:Copying TPU weights to the CPU WARNING:tensorflow:TensorFlow optimizers do not make it possible to access optimizer attributes or optimizer state after instantiation. As a result, we cannot save the optimizer as part of the model save file.You will have to compile your model again after loading it. Prefer using a Keras optimizer instead (see keras.io/optimizers).
Testing Model¶
Load model and compile again¶
In [0]:
# import tensorflow as tf
# def PSNRLoss(y_true, y_pred):
# """
# PSNR is Peek Signal to Noise Ratio, which is similar to mean squared error.
# It can be calculated as
# PSNR = 20 * log10(MAXp) - 10 * log10(MSE)
# """
# return -10. * tf.keras.backend.log(tf.keras.backend.mean(tf.keras.backend.square(y_pred - y_true))) / tf.keras.backend.log(10.)
# model_tpu = tf.keras.models.load_model('tpu_model')
# model_tpu.compile(
# optimizer=tf.train.AdamOptimizer(learning_rate=1e-3, ),
# loss='mse',
# metrics=[PSNRLoss]
# )
Prepare testing data¶
In [14]:
test_imgs = np.zeros([len(test_indices), target_shape[0], target_shape[1], 3])
test_imgs_truth = []
test_imgs_truth_size = np.zeros([len(test_indices), 2])
i = 0
print("total number of test images is " + str(len(test_indices)))
for indice in test_indices:
img_input = Image.open(path_LR + imgs_name[indice])
img_truth = Image.open(path_HR + imgs_name[indice])
img_input = img_input.resize((target_shape[1], target_shape[0]), Image.BICUBIC)
test_imgs_truth_size[i] = img_truth.size
img_input = np.array(img_input)
img_truth = np.array(img_truth)
img_input = img_input / 255.0
img_truth = img_truth / 255.0
test_imgs[i] = img_input
test_imgs_truth.append(img_truth)
i = i+1
total number of test images is 151
Predict using the trained model¶
In [15]:
cpu_model = tpu_model.sync_to_cpu()
predicted_img = cpu_model.predict(test_imgs)
INFO:tensorflow:Copying TPU weights to the CPU
The predicted output from the model is of shape target_shape = (430, 568, 3). Need to resize it for comparison
In [0]:
i = 0
model_predicted_img_real_size = []
for img in predicted_img:
temp = img*255
temp[temp > 255] = 255
temp[temp < 0] = 0
temp = Image.fromarray(temp.astype('uint8'))
temp = temp.resize(test_imgs_truth_size[i].astype('int'))
model_predicted_img_real_size.append(np.array(temp)/255.0)
i = i + 1
Take a look at the predicted images...¶
In [17]:
plot_img(model_predicted_img_real_size[9])
plot_img(test_imgs_truth[9])
In [18]:
plot_img(model_predicted_img_real_size[30])
plot_img(test_imgs_truth[30])
In [19]:
plot_img(model_predicted_img_real_size[20])
plot_img(test_imgs_truth[20])
A list of all the testing images
In [20]:
np.sort(imgs_name[test_indices])
Out[20]:
array(['img_0002.jpg', 'img_0008.jpg', 'img_0012.jpg', 'img_0024.jpg',
'img_0034.jpg', 'img_0035.jpg', 'img_0044.jpg', 'img_0046.jpg',
'img_0065.jpg', 'img_0071.jpg', 'img_0073.jpg', 'img_0106.jpg',
'img_0127.jpg', 'img_0135.jpg', 'img_0136.jpg', 'img_0151.jpg',
'img_0176.jpg', 'img_0178.jpg', 'img_0190.jpg', 'img_0192.jpg',
'img_0201.jpg', 'img_0208.jpg', 'img_0215.jpg', 'img_0235.jpg',
'img_0260.jpg', 'img_0270.jpg', 'img_0273.jpg', 'img_0274.jpg',
'img_0291.jpg', 'img_0298.jpg', 'img_0323.jpg', 'img_0325.jpg',
'img_0345.jpg', 'img_0362.jpg', 'img_0381.jpg', 'img_0392.jpg',
'img_0397.jpg', 'img_0411.jpg', 'img_0412.jpg', 'img_0439.jpg',
'img_0456.jpg', 'img_0461.jpg', 'img_0485.jpg', 'img_0496.jpg',
'img_0511.jpg', 'img_0513.jpg', 'img_0529.jpg', 'img_0532.jpg',
'img_0533.jpg', 'img_0534.jpg', 'img_0546.jpg', 'img_0547.jpg',
'img_0562.jpg', 'img_0564.jpg', 'img_0574.jpg', 'img_0576.jpg',
'img_0577.jpg', 'img_0588.jpg', 'img_0602.jpg', 'img_0618.jpg',
'img_0624.jpg', 'img_0643.jpg', 'img_0646.jpg', 'img_0657.jpg',
'img_0693.jpg', 'img_0701.jpg', 'img_0702.jpg', 'img_0705.jpg',
'img_0715.jpg', 'img_0723.jpg', 'img_0725.jpg', 'img_0727.jpg',
'img_0728.jpg', 'img_0743.jpg', 'img_0781.jpg', 'img_0785.jpg',
'img_0790.jpg', 'img_0791.jpg', 'img_0799.jpg', 'img_0807.jpg',
'img_0812.jpg', 'img_0816.jpg', 'img_0817.jpg', 'img_0823.jpg',
'img_0831.jpg', 'img_0836.jpg', 'img_0839.jpg', 'img_0840.jpg',
'img_0865.jpg', 'img_0873.jpg', 'img_0875.jpg', 'img_0893.jpg',
'img_0909.jpg', 'img_0912.jpg', 'img_0917.jpg', 'img_0922.jpg',
'img_0936.jpg', 'img_0937.jpg', 'img_0947.jpg', 'img_0949.jpg',
'img_0970.jpg', 'img_0988.jpg', 'img_0998.jpg', 'img_1023.jpg',
'img_1033.jpg', 'img_1041.jpg', 'img_1044.jpg', 'img_1045.jpg',
'img_1046.jpg', 'img_1055.jpg', 'img_1059.jpg', 'img_1064.jpg',
'img_1065.jpg', 'img_1090.jpg', 'img_1119.jpg', 'img_1164.jpg',
'img_1166.jpg', 'img_1167.jpg', 'img_1177.jpg', 'img_1181.jpg',
'img_1182.jpg', 'img_1185.jpg', 'img_1190.jpg', 'img_1200.jpg',
'img_1210.jpg', 'img_1213.jpg', 'img_1250.jpg', 'img_1261.jpg',
'img_1266.jpg', 'img_1284.jpg', 'img_1308.jpg', 'img_1313.jpg',
'img_1318.jpg', 'img_1338.jpg', 'img_1340.jpg', 'img_1352.jpg',
'img_1356.jpg', 'img_1361.jpg', 'img_1363.jpg', 'img_1367.jpg',
'img_1373.jpg', 'img_1395.jpg', 'img_1400.jpg', 'img_1401.jpg',
'img_1409.jpg', 'img_1427.jpg', 'img_1444.jpg', 'img_1454.jpg',
'img_1463.jpg', 'img_1470.jpg', 'img_1485.jpg'], dtype='<U12')
Calculate the average PSNR in testing data¶
In [0]:
def psnr(y_true, y_pred):
return -10. * np.log(np.mean(np.square(y_pred - y_true))) / np.log(10.)
psnr_predicted = np.zeros(len(test_imgs_truth))
for i in range(len(test_imgs_truth)):
psnr_predicted[i] = psnr(test_imgs_truth[i], model_predicted_img_real_size[i])
In [19]:
plt.hist(psnr_predicted, bins = 'auto')
plt.title('Distribution of PSNR for predicted testing image')
plt.show()
Comparing with results from Bicubic
In [0]:
psnr_bicubic = np.zeros(len(test_imgs_truth))
for i in range(len(test_imgs_truth)):
# read in test images and reshape it to target images
img_input = Image.open(path_LR + imgs_name[test_indices[i]])
img_input = img_input.resize(test_imgs_truth_size[i].astype('int'), Image.BICUBIC)
img_input = np.array(img_input) / 255.0
psnr_bicubic[i] = psnr(test_imgs_truth[i], img_input)
In [21]:
plt.hist(psnr_bicubic, bins = 'auto')
plt.title('Distribution of PSNR for bicubic testing image')
plt.show()
In [22]:
img_input = Image.open(path_LR + imgs_name[test_indices[0]])
plot_img(img_input)
plot_img(test_imgs_truth[0])
plot_img(model_predicted_img_real_size[0])
In [23]:
img_input = Image.open(path_LR + imgs_name[test_indices[10]])
plot_img(img_input)
plot_img(test_imgs_truth[10])
plot_img(model_predicted_img_real_size[10])
The CNN model clearly presents results that are visually better than bicubic
Run prediction for the 1000 images¶
First we need to upload the test set to Google Cloud, then load the data by the following step.
In [31]:
!gsutil cp gs://adsproject3/test_set.tar.gz dataset.tar.gz
!gunzip -c dataset.tar.gz | tar xopf -
Copying gs://adsproject3/test_set.tar.gz... \ [1 files][ 72.4 MiB/ 72.4 MiB] Operation completed over 1 objects/72.4 MiB.
In [0]:
import numpy as np
import os
from PIL import Image
path_HR = "./test_SecTwo/HR/"
path_LR = "./test_SecTwo/LR/"
target_shape = (430, 568, 3)
imgs_name = np.unique(np.array([x.replace('._','') for x in os.listdir(path_HR)]))
imgs_to_predict = imgs_name
# np.sort(imgs_name)[0:1000]
In [35]:
!mkdir predicted_images
mkdir: cannot create directory ‘predicted_images’: File exists
Make predictions (need to write a data generator since image data cannot fit in the memory)
In [0]:
def test_image_generator(input_path, output_path, names, true_image_size, batch_size = 32):
i = 0
done = False
current_size = batch_size
while True:
if((i+1)*batch_size > len(names)):
batch_paths = names[i*batch_size: len(names)]
current_size = len(names) - i*batch_size
done = True
else:
batch_paths = names[i*current_size: (i+1)*current_size]
batch_input = np.zeros([current_size, target_shape[0], target_shape[1], 3])
# batch_output = np.zeros([current_size, target_shape[0], target_shape[1], 3])
# Read in each input, perform preprocessing and get labels
j = 0
for name in batch_paths:
(input_img, output_img) = preprocess_img(name, 'normal')
batch_input[j] = input_img
# batch_output[j] = output_img
output_img = Image.open(path_HR + name)
true_image_size[i*batch_size + j] = output_img.size
j = j + 1
# Return a tuple of (input,output) to feed the network
batch_x = np.array( batch_input )
# batch_y = np.array( batch_output )
i = i + 1
# yield( batch_x, batch_y )
yield(batch_x)
if(done):
break
In [37]:
images_each_round = 100
current_images_per_round = images_each_round
for i in range(math.floor(1000/images_each_round)+1):
start_index = i*images_each_round
end_index = (i+1)*images_each_round
if(end_index > 1000):
end_index = 1000
if(start_index == 1000):
break
print('proecssing images from image '+str(start_index) + ' to image ' + str(end_index) + '.....')
true_image_size = np.zeros([current_images_per_round, 2])
# run prediction for current batch
predicted_img = cpu_model.predict_generator(
test_image_generator(path_LR, path_HR, imgs_to_predict[start_index: end_index], true_image_size, batch_size = 32),
steps = images_each_round//32 + 1
)
print('finished process current range. Now save imgs...')
# save results of current batch
j = 0
for img in predicted_img:
temp = img*255
temp[temp > 255] = 255
temp[temp < 0] = 0
temp = Image.fromarray(temp.astype('uint8'))
temp = temp.resize(true_image_size[j].astype('int'))
temp.save('predicted_images/' + imgs_to_predict[i*images_each_round+j], "JPEG", quality=80, optimize=True, progressive=True)
j = j + 1
print('saved image '+imgs_to_predict[i*images_each_round+j-1])
print('')
if (i+1) * images_each_round > 1000:
current_images_per_round = 1000 - i * images_each_round
proecssing images from image 0 to image 100..... finished process current range. Now save imgs... saved image img_0001.jpg saved image img_0002.jpg saved image img_0003.jpg saved image img_0004.jpg saved image img_0005.jpg saved image img_0006.jpg saved image img_0007.jpg saved image img_0008.jpg saved image img_0009.jpg saved image img_0010.jpg saved image img_0011.jpg saved image img_0012.jpg saved image img_0013.jpg saved image img_0014.jpg saved image img_0015.jpg saved image img_0016.jpg saved image img_0017.jpg saved image img_0018.jpg saved image img_0019.jpg saved image img_0020.jpg saved image img_0021.jpg saved image img_0022.jpg saved image img_0023.jpg saved image img_0024.jpg saved image img_0025.jpg saved image img_0026.jpg saved image img_0027.jpg saved image img_0028.jpg saved image img_0029.jpg saved image img_0030.jpg saved image img_0031.jpg saved image img_0032.jpg saved image img_0033.jpg saved image img_0034.jpg saved image img_0035.jpg saved image img_0036.jpg saved image img_0037.jpg saved image img_0038.jpg saved image img_0039.jpg saved image img_0040.jpg saved image img_0041.jpg saved image img_0042.jpg saved image img_0043.jpg saved image img_0044.jpg saved image img_0045.jpg saved image img_0046.jpg saved image img_0047.jpg saved image img_0048.jpg saved image img_0049.jpg saved image img_0050.jpg saved image img_0051.jpg saved image img_0052.jpg saved image img_0053.jpg saved image img_0054.jpg saved image img_0055.jpg saved image img_0056.jpg saved image img_0057.jpg saved image img_0058.jpg saved image img_0059.jpg saved image img_0060.jpg saved image img_0061.jpg saved image img_0062.jpg saved image img_0063.jpg saved image img_0064.jpg saved image img_0065.jpg saved image img_0066.jpg saved image img_0067.jpg saved image img_0068.jpg saved image img_0069.jpg saved image img_0070.jpg saved image img_0071.jpg saved image img_0072.jpg saved image img_0073.jpg saved image img_0074.jpg saved image img_0075.jpg saved image img_0076.jpg saved image img_0077.jpg saved image img_0078.jpg saved image img_0079.jpg saved image img_0080.jpg saved image img_0081.jpg saved image img_0082.jpg saved image img_0083.jpg saved image img_0084.jpg saved image img_0085.jpg saved image img_0086.jpg saved image img_0087.jpg saved image img_0088.jpg saved image img_0089.jpg saved image img_0090.jpg saved image img_0091.jpg saved image img_0092.jpg saved image img_0093.jpg saved image img_0094.jpg saved image img_0095.jpg saved image img_0096.jpg saved image img_0097.jpg saved image img_0098.jpg saved image img_0099.jpg saved image img_0100.jpg proecssing images from image 100 to image 200..... finished process current range. Now save imgs... saved image img_0101.jpg saved image img_0102.jpg saved image img_0103.jpg saved image img_0104.jpg saved image img_0105.jpg saved image img_0106.jpg saved image img_0107.jpg saved image img_0108.jpg saved image img_0109.jpg saved image img_0110.jpg saved image img_0111.jpg saved image img_0112.jpg saved image img_0113.jpg saved image img_0114.jpg saved image img_0115.jpg saved image img_0116.jpg saved image img_0117.jpg saved image img_0118.jpg saved image img_0119.jpg saved image img_0120.jpg saved image img_0121.jpg saved image img_0122.jpg saved image img_0123.jpg saved image img_0124.jpg saved image img_0125.jpg saved image img_0126.jpg saved image img_0127.jpg saved image img_0128.jpg saved image img_0129.jpg saved image img_0130.jpg saved image img_0131.jpg saved image img_0132.jpg saved image img_0133.jpg saved image img_0134.jpg saved image img_0135.jpg saved image img_0136.jpg saved image img_0137.jpg saved image img_0138.jpg saved image img_0139.jpg saved image img_0140.jpg saved image img_0141.jpg saved image img_0142.jpg saved image img_0143.jpg saved image img_0144.jpg saved image img_0145.jpg saved image img_0146.jpg saved image img_0147.jpg saved image img_0148.jpg saved image img_0149.jpg saved image img_0150.jpg saved image img_0151.jpg saved image img_0152.jpg saved image img_0153.jpg saved image img_0154.jpg saved image img_0155.jpg saved image img_0156.jpg saved image img_0157.jpg saved image img_0158.jpg saved image img_0159.jpg saved image img_0160.jpg saved image img_0161.jpg saved image img_0162.jpg saved image img_0163.jpg saved image img_0164.jpg saved image img_0165.jpg saved image img_0166.jpg saved image img_0167.jpg saved image img_0168.jpg saved image img_0169.jpg saved image img_0170.jpg saved image img_0171.jpg saved image img_0172.jpg saved image img_0173.jpg saved image img_0174.jpg saved image img_0175.jpg saved image img_0176.jpg saved image img_0177.jpg saved image img_0178.jpg saved image img_0179.jpg saved image img_0180.jpg saved image img_0181.jpg saved image img_0182.jpg saved image img_0183.jpg saved image img_0184.jpg saved image img_0185.jpg saved image img_0186.jpg saved image img_0187.jpg saved image img_0188.jpg saved image img_0189.jpg saved image img_0190.jpg saved image img_0191.jpg saved image img_0192.jpg saved image img_0193.jpg saved image img_0194.jpg saved image img_0195.jpg saved image img_0196.jpg saved image img_0197.jpg saved image img_0198.jpg saved image img_0199.jpg saved image img_0200.jpg proecssing images from image 200 to image 300..... finished process current range. Now save imgs... saved image img_0201.jpg saved image img_0202.jpg saved image img_0203.jpg saved image img_0204.jpg saved image img_0205.jpg saved image img_0206.jpg saved image img_0207.jpg saved image img_0208.jpg saved image img_0209.jpg saved image img_0210.jpg saved image img_0211.jpg saved image img_0212.jpg saved image img_0213.jpg saved image img_0214.jpg saved image img_0215.jpg saved image img_0216.jpg saved image img_0217.jpg saved image img_0218.jpg saved image img_0219.jpg saved image img_0220.jpg saved image img_0221.jpg saved image img_0222.jpg saved image img_0223.jpg saved image img_0224.jpg saved image img_0225.jpg saved image img_0226.jpg saved image img_0227.jpg saved image img_0228.jpg saved image img_0229.jpg saved image img_0230.jpg saved image img_0231.jpg saved image img_0232.jpg saved image img_0233.jpg saved image img_0234.jpg saved image img_0235.jpg saved image img_0236.jpg saved image img_0237.jpg saved image img_0238.jpg saved image img_0239.jpg saved image img_0240.jpg saved image img_0241.jpg saved image img_0242.jpg saved image img_0243.jpg saved image img_0244.jpg saved image img_0245.jpg saved image img_0246.jpg saved image img_0247.jpg saved image img_0248.jpg saved image img_0249.jpg saved image img_0250.jpg saved image img_0251.jpg saved image img_0252.jpg saved image img_0253.jpg saved image img_0254.jpg saved image img_0255.jpg saved image img_0256.jpg saved image img_0257.jpg saved image img_0258.jpg saved image img_0259.jpg saved image img_0260.jpg saved image img_0261.jpg saved image img_0262.jpg saved image img_0263.jpg saved image img_0264.jpg saved image img_0265.jpg saved image img_0266.jpg saved image img_0267.jpg saved image img_0268.jpg saved image img_0269.jpg saved image img_0270.jpg saved image img_0271.jpg saved image img_0272.jpg saved image img_0273.jpg saved image img_0274.jpg saved image img_0275.jpg saved image img_0276.jpg saved image img_0277.jpg saved image img_0278.jpg saved image img_0279.jpg saved image img_0280.jpg saved image img_0281.jpg saved image img_0282.jpg saved image img_0283.jpg saved image img_0284.jpg saved image img_0285.jpg saved image img_0286.jpg saved image img_0287.jpg saved image img_0288.jpg saved image img_0289.jpg saved image img_0290.jpg saved image img_0291.jpg saved image img_0292.jpg saved image img_0293.jpg saved image img_0294.jpg saved image img_0295.jpg saved image img_0296.jpg saved image img_0297.jpg saved image img_0298.jpg saved image img_0299.jpg saved image img_0300.jpg proecssing images from image 300 to image 400..... finished process current range. Now save imgs... saved image img_0301.jpg saved image img_0302.jpg saved image img_0303.jpg saved image img_0304.jpg saved image img_0305.jpg saved image img_0306.jpg saved image img_0307.jpg saved image img_0308.jpg saved image img_0309.jpg saved image img_0310.jpg saved image img_0311.jpg saved image img_0312.jpg saved image img_0313.jpg saved image img_0314.jpg saved image img_0315.jpg saved image img_0316.jpg saved image img_0317.jpg saved image img_0318.jpg saved image img_0319.jpg saved image img_0320.jpg saved image img_0321.jpg saved image img_0322.jpg saved image img_0323.jpg saved image img_0324.jpg saved image img_0325.jpg saved image img_0326.jpg saved image img_0327.jpg saved image img_0328.jpg saved image img_0329.jpg saved image img_0330.jpg saved image img_0331.jpg saved image img_0332.jpg saved image img_0333.jpg saved image img_0334.jpg saved image img_0335.jpg saved image img_0336.jpg saved image img_0337.jpg saved image img_0338.jpg saved image img_0339.jpg saved image img_0340.jpg saved image img_0341.jpg saved image img_0342.jpg saved image img_0343.jpg saved image img_0344.jpg saved image img_0345.jpg saved image img_0346.jpg saved image img_0347.jpg saved image img_0348.jpg saved image img_0349.jpg saved image img_0350.jpg saved image img_0351.jpg saved image img_0352.jpg saved image img_0353.jpg saved image img_0354.jpg saved image img_0355.jpg saved image img_0356.jpg saved image img_0357.jpg saved image img_0358.jpg saved image img_0359.jpg saved image img_0360.jpg saved image img_0361.jpg saved image img_0362.jpg saved image img_0363.jpg saved image img_0364.jpg saved image img_0365.jpg saved image img_0366.jpg saved image img_0367.jpg saved image img_0368.jpg saved image img_0369.jpg saved image img_0370.jpg saved image img_0371.jpg saved image img_0372.jpg saved image img_0373.jpg saved image img_0374.jpg saved image img_0375.jpg saved image img_0376.jpg saved image img_0377.jpg saved image img_0378.jpg saved image img_0379.jpg saved image img_0380.jpg saved image img_0381.jpg saved image img_0382.jpg saved image img_0383.jpg saved image img_0384.jpg saved image img_0385.jpg saved image img_0386.jpg saved image img_0387.jpg saved image img_0388.jpg saved image img_0389.jpg saved image img_0390.jpg saved image img_0391.jpg saved image img_0392.jpg saved image img_0393.jpg saved image img_0394.jpg saved image img_0395.jpg saved image img_0396.jpg saved image img_0397.jpg saved image img_0398.jpg saved image img_0399.jpg saved image img_0400.jpg proecssing images from image 400 to image 500..... finished process current range. Now save imgs... saved image img_0401.jpg saved image img_0402.jpg saved image img_0403.jpg saved image img_0404.jpg saved image img_0405.jpg saved image img_0406.jpg saved image img_0407.jpg saved image img_0408.jpg saved image img_0409.jpg saved image img_0410.jpg saved image img_0411.jpg saved image img_0412.jpg saved image img_0413.jpg saved image img_0414.jpg saved image img_0415.jpg saved image img_0416.jpg saved image img_0417.jpg saved image img_0418.jpg saved image img_0419.jpg saved image img_0420.jpg saved image img_0421.jpg saved image img_0422.jpg saved image img_0423.jpg saved image img_0424.jpg saved image img_0425.jpg saved image img_0426.jpg saved image img_0427.jpg saved image img_0428.jpg saved image img_0429.jpg saved image img_0430.jpg saved image img_0431.jpg saved image img_0432.jpg saved image img_0433.jpg saved image img_0434.jpg saved image img_0435.jpg saved image img_0436.jpg saved image img_0437.jpg saved image img_0438.jpg saved image img_0439.jpg saved image img_0440.jpg saved image img_0441.jpg saved image img_0442.jpg saved image img_0443.jpg saved image img_0444.jpg saved image img_0445.jpg saved image img_0446.jpg saved image img_0447.jpg saved image img_0448.jpg saved image img_0449.jpg saved image img_0450.jpg saved image img_0451.jpg saved image img_0452.jpg saved image img_0453.jpg saved image img_0454.jpg saved image img_0455.jpg saved image img_0456.jpg saved image img_0457.jpg saved image img_0458.jpg saved image img_0459.jpg saved image img_0460.jpg saved image img_0461.jpg saved image img_0462.jpg saved image img_0463.jpg saved image img_0464.jpg saved image img_0465.jpg saved image img_0466.jpg saved image img_0467.jpg saved image img_0468.jpg saved image img_0469.jpg saved image img_0470.jpg saved image img_0471.jpg saved image img_0472.jpg saved image img_0473.jpg saved image img_0474.jpg saved image img_0475.jpg saved image img_0476.jpg saved image img_0477.jpg saved image img_0478.jpg saved image img_0479.jpg saved image img_0480.jpg saved image img_0481.jpg saved image img_0482.jpg saved image img_0483.jpg saved image img_0484.jpg saved image img_0485.jpg saved image img_0486.jpg saved image img_0487.jpg saved image img_0488.jpg saved image img_0489.jpg saved image img_0490.jpg saved image img_0491.jpg saved image img_0492.jpg saved image img_0493.jpg saved image img_0494.jpg saved image img_0495.jpg saved image img_0496.jpg saved image img_0497.jpg saved image img_0498.jpg saved image img_0499.jpg saved image img_0500.jpg proecssing images from image 500 to image 600..... finished process current range. Now save imgs... saved image img_0501.jpg saved image img_0502.jpg saved image img_0503.jpg saved image img_0504.jpg saved image img_0505.jpg saved image img_0506.jpg saved image img_0507.jpg saved image img_0508.jpg saved image img_0509.jpg saved image img_0510.jpg saved image img_0511.jpg saved image img_0512.jpg saved image img_0513.jpg saved image img_0514.jpg saved image img_0515.jpg saved image img_0516.jpg saved image img_0517.jpg saved image img_0518.jpg saved image img_0519.jpg saved image img_0520.jpg saved image img_0521.jpg saved image img_0522.jpg saved image img_0523.jpg saved image img_0524.jpg saved image img_0525.jpg saved image img_0526.jpg saved image img_0527.jpg saved image img_0528.jpg saved image img_0529.jpg saved image img_0530.jpg saved image img_0531.jpg saved image img_0532.jpg saved image img_0533.jpg saved image img_0534.jpg saved image img_0535.jpg saved image img_0536.jpg saved image img_0537.jpg saved image img_0538.jpg saved image img_0539.jpg saved image img_0540.jpg saved image img_0541.jpg saved image img_0542.jpg saved image img_0543.jpg saved image img_0544.jpg saved image img_0545.jpg saved image img_0546.jpg saved image img_0547.jpg saved image img_0548.jpg saved image img_0549.jpg saved image img_0550.jpg saved image img_0551.jpg saved image img_0552.jpg saved image img_0553.jpg saved image img_0554.jpg saved image img_0555.jpg saved image img_0556.jpg saved image img_0557.jpg saved image img_0558.jpg saved image img_0559.jpg saved image img_0560.jpg saved image img_0561.jpg saved image img_0562.jpg saved image img_0563.jpg saved image img_0564.jpg saved image img_0565.jpg saved image img_0566.jpg saved image img_0567.jpg saved image img_0568.jpg saved image img_0569.jpg saved image img_0570.jpg saved image img_0571.jpg saved image img_0572.jpg saved image img_0573.jpg saved image img_0574.jpg saved image img_0575.jpg saved image img_0576.jpg saved image img_0577.jpg saved image img_0578.jpg saved image img_0579.jpg saved image img_0580.jpg saved image img_0581.jpg saved image img_0582.jpg saved image img_0583.jpg saved image img_0584.jpg saved image img_0585.jpg saved image img_0586.jpg saved image img_0587.jpg saved image img_0588.jpg saved image img_0589.jpg saved image img_0590.jpg saved image img_0591.jpg saved image img_0592.jpg saved image img_0593.jpg saved image img_0594.jpg saved image img_0595.jpg saved image img_0596.jpg saved image img_0597.jpg saved image img_0598.jpg saved image img_0599.jpg saved image img_0600.jpg proecssing images from image 600 to image 700..... finished process current range. Now save imgs... saved image img_0601.jpg saved image img_0602.jpg saved image img_0603.jpg saved image img_0604.jpg saved image img_0605.jpg saved image img_0606.jpg saved image img_0607.jpg saved image img_0608.jpg saved image img_0609.jpg saved image img_0610.jpg saved image img_0611.jpg saved image img_0612.jpg saved image img_0613.jpg saved image img_0614.jpg saved image img_0615.jpg saved image img_0616.jpg saved image img_0617.jpg saved image img_0618.jpg saved image img_0619.jpg saved image img_0620.jpg saved image img_0621.jpg saved image img_0622.jpg saved image img_0623.jpg saved image img_0624.jpg saved image img_0625.jpg saved image img_0626.jpg saved image img_0627.jpg saved image img_0628.jpg saved image img_0629.jpg saved image img_0630.jpg saved image img_0631.jpg saved image img_0632.jpg saved image img_0633.jpg saved image img_0634.jpg saved image img_0635.jpg saved image img_0636.jpg saved image img_0637.jpg saved image img_0638.jpg saved image img_0639.jpg saved image img_0640.jpg saved image img_0641.jpg saved image img_0642.jpg saved image img_0643.jpg saved image img_0644.jpg saved image img_0645.jpg saved image img_0646.jpg saved image img_0647.jpg saved image img_0648.jpg saved image img_0649.jpg saved image img_0650.jpg saved image img_0651.jpg saved image img_0652.jpg saved image img_0653.jpg saved image img_0654.jpg saved image img_0655.jpg saved image img_0656.jpg saved image img_0657.jpg saved image img_0658.jpg saved image img_0659.jpg saved image img_0660.jpg saved image img_0661.jpg saved image img_0662.jpg saved image img_0663.jpg saved image img_0664.jpg saved image img_0665.jpg saved image img_0666.jpg saved image img_0667.jpg saved image img_0668.jpg saved image img_0669.jpg saved image img_0670.jpg saved image img_0671.jpg saved image img_0672.jpg saved image img_0673.jpg saved image img_0674.jpg saved image img_0675.jpg saved image img_0676.jpg saved image img_0677.jpg saved image img_0678.jpg saved image img_0679.jpg saved image img_0680.jpg saved image img_0681.jpg saved image img_0682.jpg saved image img_0683.jpg saved image img_0684.jpg saved image img_0685.jpg saved image img_0686.jpg saved image img_0687.jpg saved image img_0688.jpg saved image img_0689.jpg saved image img_0690.jpg saved image img_0691.jpg saved image img_0692.jpg saved image img_0693.jpg saved image img_0694.jpg saved image img_0695.jpg saved image img_0696.jpg saved image img_0697.jpg saved image img_0698.jpg saved image img_0699.jpg saved image img_0700.jpg proecssing images from image 700 to image 800..... finished process current range. Now save imgs... saved image img_0701.jpg saved image img_0702.jpg saved image img_0703.jpg saved image img_0704.jpg saved image img_0705.jpg saved image img_0706.jpg saved image img_0707.jpg saved image img_0708.jpg saved image img_0709.jpg saved image img_0710.jpg saved image img_0711.jpg saved image img_0712.jpg saved image img_0713.jpg saved image img_0714.jpg saved image img_0715.jpg saved image img_0716.jpg saved image img_0717.jpg saved image img_0718.jpg saved image img_0719.jpg saved image img_0720.jpg saved image img_0721.jpg saved image img_0722.jpg saved image img_0723.jpg saved image img_0724.jpg saved image img_0725.jpg saved image img_0726.jpg saved image img_0727.jpg saved image img_0728.jpg saved image img_0729.jpg saved image img_0730.jpg saved image img_0731.jpg saved image img_0732.jpg saved image img_0733.jpg saved image img_0734.jpg saved image img_0735.jpg saved image img_0736.jpg saved image img_0737.jpg saved image img_0738.jpg saved image img_0739.jpg saved image img_0740.jpg saved image img_0741.jpg saved image img_0742.jpg saved image img_0743.jpg saved image img_0744.jpg saved image img_0745.jpg saved image img_0746.jpg saved image img_0747.jpg saved image img_0748.jpg saved image img_0749.jpg saved image img_0750.jpg saved image img_0751.jpg saved image img_0752.jpg saved image img_0753.jpg saved image img_0754.jpg saved image img_0755.jpg saved image img_0756.jpg saved image img_0757.jpg saved image img_0758.jpg saved image img_0759.jpg saved image img_0760.jpg saved image img_0761.jpg saved image img_0762.jpg saved image img_0763.jpg saved image img_0764.jpg saved image img_0765.jpg saved image img_0766.jpg saved image img_0767.jpg saved image img_0768.jpg saved image img_0769.jpg saved image img_0770.jpg saved image img_0771.jpg saved image img_0772.jpg saved image img_0773.jpg saved image img_0774.jpg saved image img_0775.jpg saved image img_0776.jpg saved image img_0777.jpg saved image img_0778.jpg saved image img_0779.jpg saved image img_0780.jpg saved image img_0781.jpg saved image img_0782.jpg saved image img_0783.jpg saved image img_0784.jpg saved image img_0785.jpg saved image img_0786.jpg saved image img_0787.jpg saved image img_0788.jpg saved image img_0789.jpg saved image img_0790.jpg saved image img_0791.jpg saved image img_0792.jpg saved image img_0793.jpg saved image img_0794.jpg saved image img_0795.jpg saved image img_0796.jpg saved image img_0797.jpg saved image img_0798.jpg saved image img_0799.jpg saved image img_0800.jpg proecssing images from image 800 to image 900..... finished process current range. Now save imgs... saved image img_0801.jpg saved image img_0802.jpg saved image img_0803.jpg saved image img_0804.jpg saved image img_0805.jpg saved image img_0806.jpg saved image img_0807.jpg saved image img_0808.jpg saved image img_0809.jpg saved image img_0810.jpg saved image img_0811.jpg saved image img_0812.jpg saved image img_0813.jpg saved image img_0814.jpg saved image img_0815.jpg saved image img_0816.jpg saved image img_0817.jpg saved image img_0818.jpg saved image img_0819.jpg saved image img_0820.jpg saved image img_0821.jpg saved image img_0822.jpg saved image img_0823.jpg saved image img_0824.jpg saved image img_0825.jpg saved image img_0826.jpg saved image img_0827.jpg saved image img_0828.jpg saved image img_0829.jpg saved image img_0830.jpg saved image img_0831.jpg saved image img_0832.jpg saved image img_0833.jpg saved image img_0834.jpg saved image img_0835.jpg saved image img_0836.jpg saved image img_0837.jpg saved image img_0838.jpg saved image img_0839.jpg saved image img_0840.jpg saved image img_0841.jpg saved image img_0842.jpg saved image img_0843.jpg saved image img_0844.jpg saved image img_0845.jpg saved image img_0846.jpg saved image img_0847.jpg saved image img_0848.jpg saved image img_0849.jpg saved image img_0850.jpg saved image img_0851.jpg saved image img_0852.jpg saved image img_0853.jpg saved image img_0854.jpg saved image img_0855.jpg saved image img_0856.jpg saved image img_0857.jpg saved image img_0858.jpg saved image img_0859.jpg saved image img_0860.jpg saved image img_0861.jpg saved image img_0862.jpg saved image img_0863.jpg saved image img_0864.jpg saved image img_0865.jpg saved image img_0866.jpg saved image img_0867.jpg saved image img_0868.jpg saved image img_0869.jpg saved image img_0870.jpg saved image img_0871.jpg saved image img_0872.jpg saved image img_0873.jpg saved image img_0874.jpg saved image img_0875.jpg saved image img_0876.jpg saved image img_0877.jpg saved image img_0878.jpg saved image img_0879.jpg saved image img_0880.jpg saved image img_0881.jpg saved image img_0882.jpg saved image img_0883.jpg saved image img_0884.jpg saved image img_0885.jpg saved image img_0886.jpg saved image img_0887.jpg saved image img_0888.jpg saved image img_0889.jpg saved image img_0890.jpg saved image img_0891.jpg saved image img_0892.jpg saved image img_0893.jpg saved image img_0894.jpg saved image img_0895.jpg saved image img_0896.jpg saved image img_0897.jpg saved image img_0898.jpg saved image img_0899.jpg saved image img_0900.jpg proecssing images from image 900 to image 1000..... finished process current range. Now save imgs... saved image img_0901.jpg saved image img_0902.jpg saved image img_0903.jpg saved image img_0904.jpg saved image img_0905.jpg saved image img_0906.jpg saved image img_0907.jpg saved image img_0908.jpg saved image img_0909.jpg saved image img_0910.jpg saved image img_0911.jpg saved image img_0912.jpg saved image img_0913.jpg saved image img_0914.jpg saved image img_0915.jpg saved image img_0916.jpg saved image img_0917.jpg saved image img_0918.jpg saved image img_0919.jpg saved image img_0920.jpg saved image img_0921.jpg saved image img_0922.jpg saved image img_0923.jpg saved image img_0924.jpg saved image img_0925.jpg saved image img_0926.jpg saved image img_0927.jpg saved image img_0928.jpg saved image img_0929.jpg saved image img_0930.jpg saved image img_0931.jpg saved image img_0932.jpg saved image img_0933.jpg saved image img_0934.jpg saved image img_0935.jpg saved image img_0936.jpg saved image img_0937.jpg saved image img_0938.jpg saved image img_0939.jpg saved image img_0940.jpg saved image img_0941.jpg saved image img_0942.jpg saved image img_0943.jpg saved image img_0944.jpg saved image img_0945.jpg saved image img_0946.jpg saved image img_0947.jpg saved image img_0948.jpg saved image img_0949.jpg saved image img_0950.jpg saved image img_0951.jpg saved image img_0952.jpg saved image img_0953.jpg saved image img_0954.jpg saved image img_0955.jpg saved image img_0956.jpg saved image img_0957.jpg saved image img_0958.jpg saved image img_0959.jpg saved image img_0960.jpg saved image img_0961.jpg saved image img_0962.jpg saved image img_0963.jpg saved image img_0964.jpg saved image img_0965.jpg saved image img_0966.jpg saved image img_0967.jpg saved image img_0968.jpg saved image img_0969.jpg saved image img_0970.jpg saved image img_0971.jpg saved image img_0972.jpg saved image img_0973.jpg saved image img_0974.jpg saved image img_0975.jpg saved image img_0976.jpg saved image img_0977.jpg saved image img_0978.jpg saved image img_0979.jpg saved image img_0980.jpg saved image img_0981.jpg saved image img_0982.jpg saved image img_0983.jpg saved image img_0984.jpg saved image img_0985.jpg saved image img_0986.jpg saved image img_0987.jpg saved image img_0988.jpg saved image img_0989.jpg saved image img_0990.jpg saved image img_0991.jpg saved image img_0992.jpg saved image img_0993.jpg saved image img_0994.jpg saved image img_0995.jpg saved image img_0996.jpg saved image img_0997.jpg saved image img_0998.jpg saved image img_0999.jpg saved image img_1000.jpg
It takes about 5min to predict and store 1000 images.
Store predicted images to local drive¶
In [38]:
!zip -r predicted_images.zip predicted_images
updating: predicted_images/ (stored 0%) updating: predicted_images/img_0629.jpg (deflated 1%) updating: predicted_images/img_0161.jpg (deflated 3%) updating: predicted_images/img_0565.jpg (deflated 1%) updating: predicted_images/img_0288.jpg (deflated 1%) updating: predicted_images/img_0948.jpg (deflated 1%) updating: predicted_images/img_0715.jpg (deflated 1%) updating: predicted_images/img_0682.jpg (deflated 1%) updating: predicted_images/img_0343.jpg (deflated 1%) updating: predicted_images/img_0604.jpg (deflated 1%) updating: predicted_images/img_0681.jpg (deflated 1%) updating: predicted_images/img_0888.jpg (deflated 1%) updating: predicted_images/img_0916.jpg (deflated 1%) updating: predicted_images/img_0100.jpg (deflated 1%) updating: predicted_images/img_0920.jpg (deflated 1%) updating: predicted_images/img_0533.jpg (deflated 1%) updating: predicted_images/img_0327.jpg (deflated 1%) updating: predicted_images/img_0808.jpg (deflated 1%) updating: predicted_images/img_0050.jpg 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predicted_images/img_0307.jpg (deflated 2%) updating: predicted_images/img_0036.jpg (deflated 1%) updating: predicted_images/img_0393.jpg (deflated 1%) updating: predicted_images/img_0624.jpg (deflated 1%) updating: predicted_images/img_0365.jpg (deflated 1%) updating: predicted_images/img_0259.jpg (deflated 1%) updating: predicted_images/img_0371.jpg (deflated 1%) updating: predicted_images/img_0133.jpg (deflated 1%) updating: predicted_images/img_0863.jpg (deflated 1%) updating: predicted_images/img_0234.jpg (deflated 1%) updating: predicted_images/img_0228.jpg (deflated 1%) updating: predicted_images/img_0840.jpg (deflated 1%) updating: predicted_images/img_0524.jpg (deflated 1%) updating: predicted_images/img_0970.jpg (deflated 1%) updating: predicted_images/img_0926.jpg (deflated 1%) updating: predicted_images/img_0021.jpg (deflated 1%) updating: predicted_images/img_0482.jpg (deflated 2%) updating: predicted_images/img_0432.jpg (deflated 1%) updating: 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In [0]:
from google.colab import files
files.download('predicted_images.zip')
In [0]: